src-python

Darknet/YOLO Command Guide for Python

Video/Webcam/Display Commands

Process a Video File

python darknet_video.py --input 2.mp4 --weights yolov4.weights --config_file yolov4.cfg --data_file obj.data

Use Display for Real-Time Detection

python darknet_video.py --input 0 --weights yolov4.weights --config_file yolov4.cfg --data_file obj.data

Use Webcam for Real-Time Detection

python darknet_video.py --input 1 --weights yolov4.weights --config_file yolov4.cfg --data_file obj.data

Save Processed Video

python darknet_video.py --input path/to/your/video.mp4 --out_filename processed_video.avi --weights yolov4.weights --config_file yolov4.cfg --data_file obj.data

Use Different YOLO Model

python darknet_video.py --input path/to/your/video.mp4 --weights path/to/your/weights.weights --config_file path/to/your/config.cfg --data_file path/to/your/data.data

Parameters

Select a Different GPU

--gpu_index 1

Change Detection Threshold

--thresh 0.3

Run in Headless Mode

--dont_show

Display Extended Output

--ext_output

Controls

• Right-click in console to stop video
• Left-click in console to resume
• Press 'q' key to stop video

Image Detection Commands

Single Image Detection

python darknet_images.py --input dog.jpg --weights yolov4.weights --config_file yolov4.cfg --data_file obj.data --gpu

Batch Image Detection from Text File

python darknet_images.py --input train.txt --weights yolov4.weights --config_file yolov4.cfg --data_file obj.data --gpu

Batch Image Detection from Folder

python darknet_images.py --input /path/to/folder --weights yolov4.weights --config_file yolov4.cfg --data_file obj.data --gpu

Change Detection Threshold for Images

python darknet_images.py --input dog.jpg --weights yolov4.weights --config_file yolov4.cfg --data_file obj.data --gpu --thresh 0.3

Apply A Different Non-Maximum Suppression (NMS)

python darknet_images.py --input dog.jpg --weights yolov4.weights --config_file yolov4.cfg --data_file obj.data --gpu --nms_thresh 0.45

Save Detection Labels

python darknet_images.py --input train.txt --weights yolov4.weights --config_file yolov4.cfg --data_file obj.data --gpu --save_labels

Display Extended Output for Images

python darknet_images.py --input dog.jpg --weights yolov4.weights --config_file yolov4.cfg --data_file obj.data --gpu --ext_output

Run Image Detection in Headless Mode

python darknet_images.py --input dog.jpg --weights yolov4.weights --config_file yolov4.cfg --data_file obj.data --gpu --dont_show

Error Handling and Notes

• Ensure correct paths, file names, and formats.
• Use Ctrl+C to stop execution in console.
• Press q to quit display windows.

Note:

Command usability depends on darknet_images.py implementation and Darknet version.